Overview

Dataset statistics

Number of variables11
Number of observations250
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.6 KiB
Average record size in memory88.5 B

Variable types

NUM11

Reproduction

Analysis started2020-08-25 00:26:12.810428
Analysis finished2020-08-25 00:26:30.220662
Duration17.41 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

oz5 is highly correlated with oz3High correlation
oz3 is highly correlated with oz5High correlation
oz1 has unique values Unique
oz2 has unique values Unique
oz3 has unique values Unique
oz4 has unique values Unique
oz5 has unique values Unique
oz6 has unique values Unique
oz7 has unique values Unique
oz8 has unique values Unique
oz9 has unique values Unique
oz10 has unique values Unique
target has unique values Unique

Variables

oz1
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.898035407066345e-09
Minimum-2.0300369262695312
Maximum2.264408826828003
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:26:30.267553image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.030036926
5-th percentile-1.547092259
Q1-0.7215948403
median-0.07870461419
Q30.8220290542
95-th percentile1.650754684
Maximum2.264408827
Range4.294445753
Interquartile range (IQR)1.543623894

Descriptive statistics

Standard deviation1.000000003
Coefficient of variation (CV)526860563
Kurtosis-0.8383125084
Mean1.898035407e-09
Median Absolute Deviation (MAD)0.7230455689
Skewness0.09102594191
Sum4.745088518e-07
Variance1.000000006
2020-08-25T00:26:30.371294image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.134764403110.4%
 
-0.440751552610.4%
 
-0.737486064410.4%
 
-0.962712228310.4%
 
0.804321110210.4%
 
-1.03550255310.4%
 
-0.268884986610.4%
 
-0.654467105910.4%
 
-0.112740509210.4%
 
0.851396918310.4%
 
-1.40068483410.4%
 
-1.44954264210.4%
 
0.0701493993410.4%
 
-0.413653999610.4%
 
-0.654238939310.4%
 
0.273906201110.4%
 
-1.93479871710.4%
 
2.06896591210.4%
 
-1.09501373810.4%
 
-0.867817342310.4%
 
-0.317208945810.4%
 
0.675342440610.4%
 
1.023160110.4%
 
0.272529214610.4%
 
1.38352525210.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-2.03003692610.4%
 
-1.93479871710.4%
 
-1.88634026110.4%
 
-1.87314200410.4%
 
-1.83809077710.4%
 
-1.82771611210.4%
 
-1.77124774510.4%
 
-1.64664542710.4%
 
-1.64589762710.4%
 
-1.58781337710.4%
 
ValueCountFrequency (%) 
2.26440882710.4%
 
2.13966941810.4%
 
2.06896591210.4%
 
1.96146345110.4%
 
1.82370734210.4%
 
1.80372130910.4%
 
1.76746261110.4%
 
1.74401068710.4%
 
1.72950649310.4%
 
1.71438622510.4%
 

oz2
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8584541976451874e-09
Minimum-1.7954281568527222
Maximum1.697510838508606
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:26:30.486137image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.795428157
5-th percentile-1.53021614
Q1-0.9059155881
median0.003722209891
Q30.898586452
95-th percentile1.597565627
Maximum1.697510839
Range3.492938995
Interquartile range (IQR)1.80450204

Descriptive statistics

Standard deviation0.9999999975
Coefficient of variation (CV)538081594.2
Kurtosis-1.208847917
Mean1.858454198e-09
Median Absolute Deviation (MAD)0.903472513
Skewness0.0008608865263
Sum4.646135494e-07
Variance0.999999995
2020-08-25T00:26:30.591179image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1.06640338910.4%
 
1.58625066310.4%
 
1.09293651610.4%
 
-0.434009343410.4%
 
1.03870475310.4%
 
0.362881720110.4%
 
0.21345734610.4%
 
-0.905934512610.4%
 
-0.7125702510.4%
 
-1.69564604810.4%
 
0.137980818710.4%
 
-1.76204764810.4%
 
-0.342602163610.4%
 
-1.47395205510.4%
 
1.03156554710.4%
 
1.5862495910.4%
 
0.215362906510.4%
 
1.47687089410.4%
 
0.24893939510.4%
 
0.366381138610.4%
 
0.554833114110.4%
 
-1.36649620510.4%
 
0.948871493310.4%
 
-0.801507711410.4%
 
0.899061679810.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-1.79542815710.4%
 
-1.7938462510.4%
 
-1.76204764810.4%
 
-1.74171900710.4%
 
-1.69564604810.4%
 
-1.69493126910.4%
 
-1.65793228110.4%
 
-1.59914028610.4%
 
-1.59771704710.4%
 
-1.58475828210.4%
 
ValueCountFrequency (%) 
1.69751083910.4%
 
1.69277465310.4%
 
1.68313527110.4%
 
1.67764711410.4%
 
1.67029094710.4%
 
1.66835272310.4%
 
1.66520011410.4%
 
1.63066959410.4%
 
1.63052201310.4%
 
1.62689220910.4%
 

oz3
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.419576048851013e-09
Minimum-2.142465591430664
Maximum3.431476831436157
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:26:30.703982image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.142465591
5-th percentile-1.309037256
Q1-0.7542047054
median-0.1531300843
Q30.6306185573
95-th percentile1.88115505
Maximum3.431476831
Range5.573942423
Interquartile range (IQR)1.384823263

Descriptive statistics

Standard deviation1.000000001
Coefficient of variation (CV)-413295544.8
Kurtosis0.3873240118
Mean-2.419576049e-09
Median Absolute Deviation (MAD)0.6768637598
Skewness0.7409068568
Sum-6.048940122e-07
Variance1.000000002
2020-08-25T00:26:30.810954image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.28638732410.4%
 
-0.830729544210.4%
 
-0.634076654910.4%
 
-0.613957822310.4%
 
0.0181179996610.4%
 
-0.392914503810.4%
 
0.10422556110.4%
 
-1.06799745610.4%
 
1.5946214210.4%
 
-0.939277410510.4%
 
-0.49642527110.4%
 
1.18296480210.4%
 
-0.607104599510.4%
 
1.74740803210.4%
 
-0.3499372910.4%
 
2.00130796410.4%
 
-0.220369070810.4%
 
0.989894747710.4%
 
-0.0840650126310.4%
 
1.47394335310.4%
 
-0.177650466610.4%
 
-0.647045433510.4%
 
-1.41669309110.4%
 
-1.32646751410.4%
 
0.876996755610.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-2.14246559110.4%
 
-1.95913219510.4%
 
-1.73274862810.4%
 
-1.71968877310.4%
 
-1.61666619810.4%
 
-1.52521419510.4%
 
-1.43687856210.4%
 
-1.41669309110.4%
 
-1.36454129210.4%
 
-1.34659099610.4%
 
ValueCountFrequency (%) 
3.43147683110.4%
 
3.35664606110.4%
 
2.74504089410.4%
 
2.56685924510.4%
 
2.49301242810.4%
 
2.21912574810.4%
 
2.21782374410.4%
 
2.12697958910.4%
 
2.08423137710.4%
 
2.00130796410.4%
 

oz4
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1560117602348327e-09
Minimum-1.7471628189086914
Maximum1.7593257427215576
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:26:30.925956image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.747162819
5-th percentile-1.581185299
Q1-0.7965516299
median-4.334037658e-05
Q30.8291812539
95-th percentile1.56000632
Maximum1.759325743
Range3.506488562
Interquartile range (IQR)1.625732884

Descriptive statistics

Standard deviation0.9999999954
Coefficient of variation (CV)463819360.3
Kurtosis-1.123836443
Mean2.15601176e-09
Median Absolute Deviation (MAD)0.8276165802
Skewness-0.01880027113
Sum5.390029401e-07
Variance0.9999999908
2020-08-25T00:26:31.038076image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1.54589688810.4%
 
-1.27598798310.4%
 
-1.11884844310.4%
 
-0.476655244810.4%
 
0.0362210273710.4%
 
1.54235434510.4%
 
1.70054960310.4%
 
-0.295009255410.4%
 
1.04165184510.4%
 
-0.460533589110.4%
 
-1.67513990410.4%
 
-0.920984983410.4%
 
-0.372397363210.4%
 
-1.60384285410.4%
 
-1.38411104710.4%
 
1.74583566210.4%
 
-0.663213133810.4%
 
1.07453274710.4%
 
0.77017229810.4%
 
0.452548325110.4%
 
-0.0229232963210.4%
 
0.763818860110.4%
 
-1.33524906610.4%
 
-0.748678684210.4%
 
1.27664935610.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-1.74716281910.4%
 
-1.73835587510.4%
 
-1.7233632810.4%
 
-1.72069251510.4%
 
-1.70826995410.4%
 
-1.68916726110.4%
 
-1.67513990410.4%
 
-1.65268385410.4%
 
-1.60930168610.4%
 
-1.60892164710.4%
 
ValueCountFrequency (%) 
1.75932574310.4%
 
1.7462068810.4%
 
1.74583566210.4%
 
1.70054960310.4%
 
1.68387281910.4%
 
1.6795096410.4%
 
1.64278495310.4%
 
1.63810145910.4%
 
1.62522065610.4%
 
1.62112915510.4%
 

oz5
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.0628795027732848e-09
Minimum-1.4463292360305786
Maximum4.728042602539063
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:26:31.156290image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.446329236
5-th percentile-1.052477181
Q1-0.7032560855
median-0.3222602606
Q30.5295671374
95-th percentile1.893118727
Maximum4.728042603
Range6.174371839
Interquartile range (IQR)1.232823223

Descriptive statistics

Standard deviation0.9999999906
Coefficient of variation (CV)-484759284
Kurtosis3.023715628
Mean-2.062879503e-09
Median Absolute Deviation (MAD)0.5205205306
Skewness1.527043195
Sum-5.157198757e-07
Variance0.9999999813
2020-08-25T00:26:31.257673image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.0896064713610.4%
 
-0.682298421910.4%
 
0.450767934310.4%
 
-0.541693925910.4%
 
-0.694523930510.4%
 
4.72804260310.4%
 
-1.02788019210.4%
 
0.401764839910.4%
 
-0.308379918310.4%
 
-0.270649284110.4%
 
-0.428067952410.4%
 
-0.613947570310.4%
 
-0.117653772210.4%
 
0.53240197910.4%
 
3.68429160110.4%
 
0.605597913310.4%
 
2.15680575410.4%
 
-1.0696703210.4%
 
-1.19759535810.4%
 
-0.0809531956910.4%
 
-0.808775842210.4%
 
-1.29230344310.4%
 
-0.692536592510.4%
 
0.362136542810.4%
 
-0.28864896310.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-1.44632923610.4%
 
-1.37916028510.4%
 
-1.34754717410.4%
 
-1.29230344310.4%
 
-1.25174009810.4%
 
-1.19759535810.4%
 
-1.1722685110.4%
 
-1.16080772910.4%
 
-1.15729296210.4%
 
-1.10146009910.4%
 
ValueCountFrequency (%) 
4.72804260310.4%
 
3.68896317510.4%
 
3.68429160110.4%
 
3.25811958310.4%
 
3.12896561610.4%
 
2.90144348110.4%
 
2.30656409310.4%
 
2.30576109910.4%
 
2.17999768310.4%
 
2.15680575410.4%
 

oz6
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2379605323076249e-09
Minimum-1.997885465621948
Maximum1.7213869094848633
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:26:31.366245image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.997885466
5-th percentile-1.68812024
Q1-0.7546154559
median0.02734936494
Q30.8455403298
95-th percentile1.522059631
Maximum1.721386909
Range3.719272375
Interquartile range (IQR)1.600155786

Descriptive statistics

Standard deviation0.9999999994
Coefficient of variation (CV)807780194.4
Kurtosis-0.9620301165
Mean1.237960532e-09
Median Absolute Deviation (MAD)0.8069437919
Skewness-0.2007386286
Sum3.094901331e-07
Variance0.9999999988
2020-08-25T00:26:31.474998image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.620115339810.4%
 
1.42412996310.4%
 
1.5277175910.4%
 
0.775569379310.4%
 
-0.746757030510.4%
 
0.927861511710.4%
 
0.0599389113510.4%
 
-1.5999462610.4%
 
1.07793569610.4%
 
-0.548017501810.4%
 
0.14188657710.4%
 
0.827228844210.4%
 
-1.94736766810.4%
 
1.65710604210.4%
 
-0.454003274410.4%
 
0.828274369210.4%
 
-1.65015780910.4%
 
-0.345483899110.4%
 
-0.432202100810.4%
 
1.55595231110.4%
 
-0.140294432610.4%
 
1.04910898210.4%
 
-0.676409304110.4%
 
0.691173851510.4%
 
-0.487124830510.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-1.99788546610.4%
 
-1.94736766810.4%
 
-1.92232525310.4%
 
-1.90013861710.4%
 
-1.87539219910.4%
 
-1.87212002310.4%
 
-1.85598468810.4%
 
-1.84831595410.4%
 
-1.77032077310.4%
 
-1.75965273410.4%
 
ValueCountFrequency (%) 
1.72138690910.4%
 
1.70091474110.4%
 
1.6884964710.4%
 
1.68248701110.4%
 
1.65833556710.4%
 
1.65710604210.4%
 
1.6291428810.4%
 
1.62157559410.4%
 
1.60789036810.4%
 
1.60364925910.4%
 

oz7
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.295740604400635e-10
Minimum-1.8686106204986568
Maximum1.6768662929534912
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:26:31.594792image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.86861062
5-th percentile-1.691995889
Q1-0.8256972879
median0.0494037848
Q30.8351482302
95-th percentile1.509281421
Maximum1.676866293
Range3.545476913
Interquartile range (IQR)1.660845518

Descriptive statistics

Standard deviation1.000000003
Coefficient of variation (CV)1588375484
Kurtosis-1.062024821
Mean6.295740604e-10
Median Absolute Deviation (MAD)0.8472037911
Skewness-0.08776768953
Sum1.573935151e-07
Variance1.000000006
2020-08-25T00:26:31.700337image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.143189042810.4%
 
0.116595119210.4%
 
-1.18589353610.4%
 
-0.856129229110.4%
 
1.11850225910.4%
 
1.12142169510.4%
 
0.506021618810.4%
 
0.802894890310.4%
 
1.08430397510.4%
 
1.610017310.4%
 
-0.58776277310.4%
 
-0.18241116410.4%
 
0.916842639410.4%
 
-0.315991461310.4%
 
-1.84305965910.4%
 
-0.61195921910.4%
 
-0.198957160110.4%
 
0.305979102810.4%
 
0.0893091112410.4%
 
-0.791152298510.4%
 
0.749865710710.4%
 
-0.528453528910.4%
 
0.652325451410.4%
 
-0.901009321210.4%
 
-0.0441975444610.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-1.8686106210.4%
 
-1.86375284210.4%
 
-1.85531175110.4%
 
-1.84636461710.4%
 
-1.84305965910.4%
 
-1.84054899210.4%
 
-1.832822810.4%
 
-1.81626653710.4%
 
-1.77824854910.4%
 
-1.7681939610.4%
 
ValueCountFrequency (%) 
1.67686629310.4%
 
1.67161202410.4%
 
1.64803636110.4%
 
1.64174795210.4%
 
1.62096679210.4%
 
1.610017310.4%
 
1.59396231210.4%
 
1.56536495710.4%
 
1.54312241110.4%
 
1.5248953110.4%
 

oz8
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.539476215839386e-09
Minimum-1.7820769548416138
Maximum1.654485106468201
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:26:31.821676image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.782076955
5-th percentile-1.621470797
Q1-0.8431179225
median0.01294437703
Q30.8141089529
95-th percentile1.504898137
Maximum1.654485106
Range3.436562061
Interquartile range (IQR)1.657226875

Descriptive statistics

Standard deviation0.9999999988
Coefficient of variation (CV)-649571580.6
Kurtosis-1.186771141
Mean-1.539476216e-09
Median Absolute Deviation (MAD)0.8486192292
Skewness-0.07481055904
Sum-3.84869054e-07
Variance0.9999999977
2020-08-25T00:26:31.928910image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.952638566510.4%
 
-1.17825090910.4%
 
0.721385359810.4%
 
1.01601135710.4%
 
0.03440526510.4%
 
0.663076698810.4%
 
0.733767390310.4%
 
-0.207238793410.4%
 
1.26037085110.4%
 
0.698914647110.4%
 
1.03134238710.4%
 
-0.398282766310.4%
 
-0.508475601710.4%
 
1.58823943110.4%
 
-1.67353057910.4%
 
1.56445014510.4%
 
0.153110697910.4%
 
-1.61100673710.4%
 
1.05500638510.4%
 
-1.24738144910.4%
 
0.531404674110.4%
 
0.816175818410.4%
 
-1.49444341710.4%
 
0.735123455510.4%
 
0.484204292310.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-1.78207695510.4%
 
-1.76341211810.4%
 
-1.76323819210.4%
 
-1.76068043710.4%
 
-1.75668120410.4%
 
-1.75281107410.4%
 
-1.73734259610.4%
 
-1.73461246510.4%
 
-1.73393166110.4%
 
-1.67353057910.4%
 
ValueCountFrequency (%) 
1.65448510610.4%
 
1.65376520210.4%
 
1.62489235410.4%
 
1.60611486410.4%
 
1.60205531110.4%
 
1.58823943110.4%
 
1.57685470610.4%
 
1.5669610510.4%
 
1.56445014510.4%
 
1.53682136510.4%
 

oz9
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.726783562451601e-10
Minimum-1.6779357194900513
Maximum1.7170368432998655
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:26:32.047786image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.677935719
5-th percentile-1.527322668
Q1-0.8857057244
median-0.007280433085
Q30.8501047045
95-th percentile1.565946501
Maximum1.717036843
Range3.394972563
Interquartile range (IQR)1.735810429

Descriptive statistics

Standard deviation1.000000002
Coefficient of variation (CV)1145897563
Kurtosis-1.212647922
Mean8.726783562e-10
Median Absolute Deviation (MAD)0.8697195887
Skewness-0.004471711901
Sum2.181695891e-07
Variance1.000000004
2020-08-25T00:26:32.154938image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1.10058474510.4%
 
-1.20835113510.4%
 
-1.04630124610.4%
 
-0.00112606026210.4%
 
-0.0589112602210.4%
 
0.451270222710.4%
 
0.285414636110.4%
 
0.534370303210.4%
 
0.588078379610.4%
 
-1.29431760310.4%
 
0.0921251997410.4%
 
-0.196577906610.4%
 
0.766290545510.4%
 
0.0111569203410.4%
 
1.60754096510.4%
 
-0.0898652002210.4%
 
0.101973250510.4%
 
-1.24629521410.4%
 
1.14778006110.4%
 
-0.326505750410.4%
 
-0.626133859210.4%
 
1.70476710810.4%
 
0.928955078110.4%
 
0.322829246510.4%
 
0.731106281310.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-1.67793571910.4%
 
-1.65243828310.4%
 
-1.64733445610.4%
 
-1.64333450810.4%
 
-1.64029765110.4%
 
-1.62291407610.4%
 
-1.60132074410.4%
 
-1.58706164410.4%
 
-1.56254768410.4%
 
-1.55857849110.4%
 
ValueCountFrequency (%) 
1.71703684310.4%
 
1.70476710810.4%
 
1.70202219510.4%
 
1.69983768510.4%
 
1.69357049510.4%
 
1.68957507610.4%
 
1.67930901110.4%
 
1.63828897510.4%
 
1.62277519710.4%
 
1.60754096510.4%
 

oz10
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.753229977476735e-09
Minimum-1.6619360446929932
Maximum1.706633448600769
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:26:32.273675image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-1.661936045
5-th percentile-1.509601742
Q1-0.8998338729
median0.01449158438
Q30.8584515005
95-th percentile1.562135506
Maximum1.706633449
Range3.368569493
Interquartile range (IQR)1.758285373

Descriptive statistics

Standard deviation0.999999999
Coefficient of variation (CV)-266437176.8
Kurtosis-1.234042448
Mean-3.753229977e-09
Median Absolute Deviation (MAD)0.8991668189
Skewness0.01075632484
Sum-9.383074944e-07
Variance0.999999998
2020-08-25T00:26:32.379263image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-0.930175185210.4%
 
-1.52666759510.4%
 
1.40366256210.4%
 
0.433020144710.4%
 
0.702345907710.4%
 
1.25212752810.4%
 
1.17903554410.4%
 
-0.380674272810.4%
 
-1.18293845710.4%
 
-1.49543523810.4%
 
-0.843907475510.4%
 
-0.066059134910.4%
 
-0.407162338510.4%
 
-1.63416945910.4%
 
-1.22100555910.4%
 
-1.25029468510.4%
 
0.0226014424110.4%
 
1.05732619810.4%
 
0.410716235610.4%
 
0.843402922210.4%
 
-0.946251273210.4%
 
-1.62960350510.4%
 
0.204433426310.4%
 
1.60574293110.4%
 
0.931764900710.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-1.66193604510.4%
 
-1.63893771210.4%
 
-1.63416945910.4%
 
-1.63084864610.4%
 
-1.62960350510.4%
 
-1.60953295210.4%
 
-1.59904336910.4%
 
-1.54561591110.4%
 
-1.54540133510.4%
 
-1.53787612910.4%
 
ValueCountFrequency (%) 
1.70663344910.4%
 
1.68905210510.4%
 
1.68895912210.4%
 
1.68578004810.4%
 
1.63514137310.4%
 
1.62167525310.4%
 
1.62110662510.4%
 
1.62053501610.4%
 
1.60948073910.4%
 
1.60574293110.4%
 

target
Real number (ℝ)

UNIQUE

Distinct count250
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4717301094767665e-09
Minimum-2.271225690841675
Maximum3.4070088863372803
Zeros0
Zeros (%)0.0%
Memory size2.1 KiB
2020-08-25T00:26:32.505743image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum-2.271225691
5-th percentile-1.747781181
Q1-0.6869943887
median0.1237290688
Q30.6437554657
95-th percentile1.365865302
Maximum3.407008886
Range5.678234577
Interquartile range (IQR)1.330749854

Descriptive statistics

Standard deviation0.9999999966
Coefficient of variation (CV)404574914
Kurtosis0.2666405446
Mean2.471730109e-09
Median Absolute Deviation (MAD)0.6459429078
Skewness0.04678846723
Sum6.179325274e-07
Variance0.9999999931
2020-08-25T00:26:32.774346image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-1.60742175610.4%
 
0.327813863810.4%
 
0.813666522510.4%
 
0.313720643510.4%
 
0.103431217410.4%
 
0.57597225910.4%
 
-0.265157520810.4%
 
-0.794618129710.4%
 
0.496979147210.4%
 
-0.165205925710.4%
 
-1.12193632110.4%
 
-0.0777806863210.4%
 
0.590018212810.4%
 
0.518726468110.4%
 
-1.10678648910.4%
 
-0.731124997110.4%
 
-1.04910576310.4%
 
0.950850248310.4%
 
0.518720269210.4%
 
0.241129934810.4%
 
0.775297224510.4%
 
0.308763891510.4%
 
-0.728671252710.4%
 
0.505035936810.4%
 
0.63052332410.4%
 
Other values (225)22590.0%
 
ValueCountFrequency (%) 
-2.27122569110.4%
 
-2.17684793510.4%
 
-2.04458212910.4%
 
-2.01065468810.4%
 
-2.00995302210.4%
 
-1.99813485110.4%
 
-1.8495016110.4%
 
-1.84330356110.4%
 
-1.82187759910.4%
 
-1.80653667410.4%
 
ValueCountFrequency (%) 
3.40700888610.4%
 
3.12837338410.4%
 
2.83057618110.4%
 
2.50775146510.4%
 
2.41535401310.4%
 
1.83464944410.4%
 
1.75432288610.4%
 
1.64894056310.4%
 
1.5023157610.4%
 
1.46337699910.4%
 

Interactions

2020-08-25T00:26:13.279192image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:13.388685image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:13.506361image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:13.794545image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:13.913318image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:14.028847image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:14.153210image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:14.274161image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:14.391185image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:14.508769image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:14.629220image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:14.743894image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:14.865337image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:14.993846image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:15.117941image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:15.249648image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:15.377470image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:15.509468image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:15.642148image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:15.773015image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:15.901903image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:16.031018image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:16.157609image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:16.275931image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:16.400451image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:16.517766image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:16.648886image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:16.771193image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:16.898196image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:17.024717image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:17.148894image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:17.274777image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:17.397049image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:17.702314image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:17.823318image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:17.954035image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:18.080108image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:18.208959image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:18.336344image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:18.468759image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:18.601439image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:18.729907image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:18.859875image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:18.988790image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:19.114002image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:19.233001image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:19.359584image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:19.480908image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:19.609527image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:19.734801image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:19.865464image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:19.997519image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:20.125061image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:20.252798image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:20.379062image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:20.500996image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:20.626024image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:20.760436image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:20.888805image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:21.022380image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:21.154098image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:21.295208image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:21.603575image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:21.738660image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:21.878678image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:22.012740image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:22.143418image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:22.270221image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:22.404271image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:22.532656image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:22.666937image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:22.804314image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:22.945798image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:23.084153image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:23.221204image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:23.359816image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:23.494933image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:23.634107image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:23.760214image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:23.890798image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:24.015509image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:24.146830image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:24.275381image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:24.409712image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:24.542544image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:24.677326image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:24.807914image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:24.936884image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:25.062986image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:25.185462image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:25.318515image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:25.638442image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:25.770214image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:25.903084image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:26.037908image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:26.185200image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:26.316662image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:26.457067image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:26.595002image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:26.721535image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:26.846238image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:26.979959image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:27.105808image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:27.236446image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:27.367574image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:27.501935image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:27.636009image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:27.766916image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:27.905214image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:28.036134image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:28.162406image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:28.280719image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:28.410239image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:28.528894image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:28.654555image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:28.777970image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:28.912086image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:29.048754image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:29.171954image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:29.305404image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:29.612859image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T00:26:32.899856image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-25T00:26:33.125683image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-25T00:26:33.355622image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-25T00:26:33.581312image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-08-25T00:26:29.844659image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T00:26:30.122113image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

oz1oz2oz3oz4oz5oz6oz7oz8oz9oz10target
00.7044831.1495490.656861-0.1760720.5210630.698100-1.159289-0.4228810.7199410.745729-1.713511
10.392924-0.043664-0.310236-1.313376-0.474012-0.748451-0.538037-0.479377-1.456045-0.073238-0.589628
2-1.384145-0.949839-1.364541-1.325709-0.887728-1.4851360.2921120.083464-0.5225421.5177870.253572
30.8774901.6776470.6966160.1958740.7028921.5692071.5059880.985557-0.992020-0.141155-0.193310
4-0.259140-0.905935-0.636095-0.339005-0.541799-0.041791-0.901009-1.5610750.296830-1.0842780.518726
5-1.838091-1.793846-0.771661-0.554281-0.794080-0.052422-0.4815400.019818-1.558578-1.661936-0.728671
6-0.855772-0.387296-0.220369-0.581764-0.428068-0.154900-1.6468431.654485-0.3673841.3823940.301318
7-0.3598910.371541-0.703939-0.666856-0.4150411.2775861.366366-1.104586-0.766601-0.5361410.228939
81.4733381.5862511.7172010.7701722.003042-0.248305-0.498148-0.4564191.549641-1.4684821.834649
9-1.886340-1.3746120.0924661.441013-0.7359241.273117-0.3383041.226742-0.865475-0.9462510.018026

Last rows

oz1oz2oz3oz4oz5oz6oz7oz8oz9oz10target
2400.2584241.054223-0.646415-1.294447-0.377289-0.8289470.749866-1.122312-0.353174-1.222488-1.788953
241-0.445201-0.875074-0.4863110.394501-0.6410290.6724411.5248951.044259-0.2854010.4499480.630523
2420.4979830.160634-0.941794-1.296186-0.641076-0.5081821.5060841.1049690.383136-1.330587-0.981400
2431.8037211.2506542.7450411.2766493.6842920.980925-0.366772-0.397667-0.6992560.2360272.830576
244-0.531335-1.036029-0.527650-1.501649-0.745900-0.823081-1.6177310.6498301.260324-0.365122-0.210119
2450.220682-0.4228590.1606470.824357-0.096111-1.754764-0.8561291.3712870.5332121.6205350.959996
246-0.705398-1.484789-0.712263-1.689167-0.8576260.3742451.3288230.7144611.059302-1.445145-0.836932
2471.4356360.8464831.0045030.6097301.085554-1.439002-0.676975-0.9671251.5036661.403063-0.656361
248-1.350098-1.406449-1.301276-0.883528-0.6822981.205429-0.516817-0.774341-1.538144-1.495435-0.077781
249-1.491922-1.166471-0.9406580.716289-0.647987-1.099591-1.1858941.6248920.9215960.0244050.327814